Efficient distributed processing for large scale MIMO detection


Téléchargements par mois depuis la dernière année

Plus de statistiques...

Ahmed Ouameur, M. et Massicotte, D. (2019, September 2-6). Efficient distributed processing for large scale MIMO detection. Dans 2019 27th European Signal Processing Conference (EUSIPCO), A Coruna, Spain DOI 10.23919/EUSIPCO.2019.8902787.

[thumbnail of MASSICOTTE_D_26_POST.pdf]
Télécharger (242kB) | Prévisualisation


In large scale multiple-input multiple-output (MIMO), high spectral and energy efficiencies comes at the expense of a high computational complexity baseband processing. Many contributions have been proposed to reduce such complexity using matrix inversion approximation techniques for instance. On the other hand, to reduce the constraint on the interconnects' bandwidth, fewer decentralized processing techniques have emerged. Here, we propose a computationally efficient technique based on embedding one single Gauss-Seidel iteration within every ADMM based detection iteration. The simulations are performed using an LTE-like TDD-OFDM frame structure and waveform, under perfect and non-perfect channel state information (CSI). Early results reveal that the proposed ADMM-GS algorithm can outperform the centralised GS based technique processing in a high SNR region and high load regime. In addition ADMM-GS' performance exhibits relatively less sensitivity to channel estimation error; a characteristic inherited from the centralised GS technique.

Type de document: Document issu d'une conférence ou d'un atelier
Mots-clés libres: Large scale multiple-input multiple-output (MIMO) Zero forcing (ZF) detection Maximum ratio combining (MRC) Receiver combining Gauss Seidel (GS) Alternating direction method of multipliers (ADMM)
Date de dépôt: 09 mai 2022 12:10
Dernière modification: 09 mai 2022 15:26
URI: https://depot-e.uqtr.ca/id/eprint/10142

Actions (administrateurs uniquement)

Éditer la notice Éditer la notice